A general approach for matching two shapes is based on the estimation of a deformation (a diffeomorphism) transforming the first one into the second one. We developed a new framework in order to build diffeomorphisms so that a prior on deformation patterns can be easily incorporated. This prior can for instance correspond to an additional knowledge one has on the data under study. Our framework is based on the notion of deformation modules which are structures capable of generating vector fields of a particular chosen type and parametrized in small dimension. Several deformation modules can combine and interact in order to general a multi-modular diffeomorphisms. I will present how this framework allows to incorporate a prior in a deformation model by first building an adapted deformation module and then using it to build modular deformations.

This presentation is part of Minisymposium “MS75 - Geometric methods for shape analysis with applications to biomedical imaging and computational anatomy, Part II (2 parts)”
organized by: Joan Alexis Glaunès (MAP5, Université Paris Descartes) , Sergey Kushnarev (Singapore University of Technology and Design) , Mario Micheli (Harvey Mudd College) .

Authors:

Barbara Gris (Laboratoire Jacques-Louis Lions)

Keywords:

image registration